datasetsSearch the Sonoma County Open Data catalog of open datasets by keyword. Returns each dataset's resource_id, name, description, category and update date — pass the resource_id to query/metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword to search dataset titles/descriptions (e.g. "budget", "crime", "health"). | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "budget"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "crime"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, openWorld, idempotent, and nondestructive. The description adds that it returns specific fields (resource_id, name, description, category, update date) and hints at next steps, providing behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with a helpful follow-up note. It is front-loaded with the core purpose and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description explains the return fields and suggests a workflow (pass resource_id to 'query/metadata'). This provides adequate completeness for a search tool, though pagination details are left to the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the input schema describes each parameter well. The description adds only that the tool searches by keyword, which aligns with the query parameter but does not add new meaning for the parameters themselves. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the Sonoma County Open Data catalog by keyword, which is a specific resource and action. It distinguishes from siblings like 'search_within' by being specific to this catalog.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching datasets but does not explicitly state when to use this tool versus alternatives among the 30+ sibling tools. No when-not-to-use or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded reuses the same router, and deep_research overlaps with ask_pipeworx for multi-part questions. Similarly, bet_research, polymarket_edges, polymarket_arbitrage, entity_profile, compare_entities, recent_changes, and resolve_entity all cluster around overlapping research/comparison tasks despite detailed descriptions.
Names are consistently lowercase snake_case, but the naming pattern is mixed: some are verb_noun (ask_pipeworx, validate_claim, resolve_entity), some are bare nouns (datasets, metadata, polymarket_edges), some are imperative verbs (remember, forget, query), and some are adjective_noun (recent_alerts, recent_changes). The polymarket_* and pipeworx_* prefixes help, but the overall convention is not uniform.
With 34 tools, the server exceeds the 25+ threshold and feels overstuffed for a coherent single-purpose MCP server. It spans unrelated domains: Sonoma County open data, a general structured-data router, prediction-market analysis, memory, subscriptions, npm dependency checking, and llms.txt generation—each could reasonably be its own smaller server.
The core research workflow is well covered: routing, grounded verification, entity resolution, entity profiles, comparisons, recent changes, claim validation, memory, subscriptions, and prediction-market execution checks are all present. Minor gaps exist, such as no subscription update/edit, no general pipeworx:// record-reader tool, and a read-only open-data surface, but agents can usually work around these.